# uamcf/methods/distillation.py
import numpy as np
from typing import Dict, Any, Optional, List, Tuple, Union

from .base import CompressionMethod


class Distillation(CompressionMethod):
    """实现知识蒸馏的压缩方法"""

    def __init__(self, config: Optional[Dict] = None):
        """
        初始化知识蒸馏方法

        Args:
            config: 蒸馏配置参数，包括：
                - temperature: 蒸馏温度 (float, 默认: 2.0)
                - alpha: 蒸馏损失权重 (float, 0-1之间, 默认: 0.5)
                - features: 是否使用特征蒸馏 (bool, 默认: False)
                - iterations: 蒸馏训练迭代次数 (int, 默认: 100)
                - batch_size: 批处理大小 (int, 默认: 64)
                - teacher_model: 教师模型 (默认: None)
        """
        super().__init__(config)
        self.temperature = self.config.get("temperature", 2.0)
        self.alpha = self.config.get("alpha", 0.5)
        self.features = self.config.get("features", False)
        self.iterations = self.config.get("iterations", 100)
        self.batch_size = self.config.get("batch_size", 64)
        self.teacher_model = self.config.get("teacher_model", None)

        self.logger.info(f"Initialized knowledge distillation with temperature={self.temperature}, alpha={self.alpha}")

    def compress(self, layer: Any) -> Any:
        """
        对层应用知识蒸馏

        Note: 知识蒸馏通常应用于整个模型，而不是单个层。
              这里为了保持接口一致性，实现了单层蒸馏逻辑，
              但实际应用中可能需要由适配器扩展此方法。

        Args:
            layer: 要蒸馏的层

        Returns:
            蒸馏后的层
        """
        self.logger.info(f"Applying knowledge distillation with temperature={self.temperature}")

        # 知识蒸馏通常需要训练数据和教师模型
        # 这里仅提供概念性实现，实际操作将由适配器处理

        if self.teacher_model is None:
            self.logger.warning("No teacher model provided for distillation")
            return layer

        try:
            # 记录原始层用于评估
            self.original_layer = layer

            # 实际蒸馏逻辑取决于框架，这里提供伪代码
            # 1. 获取教师模型中对应的层
            teacher_layer = self._get_corresponding_teacher_layer(layer)

            if teacher_layer is None:
                self.logger.warning("Could not find corresponding teacher layer")
                return layer

            # 2. 对于卷积或全连接层，可以通过匹配输出分布进行蒸馏
            if hasattr(layer, "weight"):
                # 记录蒸馏参数
                layer.distillation_info = {
                    "temperature": self.temperature,
                    "alpha": self.alpha,
                    "method": "layer_distillation"
                }

                # 注意：实际的蒸馏过程需要训练数据和反向传播
                self.logger.info("Knowledge distillation requires training data, process delegated to adapter")

            return layer

        except Exception as e:
            self.logger.error(f"Distillation failed: {str(e)}")
            return layer

    def _get_corresponding_teacher_layer(self, student_layer: Any) -> Any:
        """查找教师模型中对应的层"""
        # 实际实现需要框架支持，这里只是概念演示
        if not self.teacher_model:
            return None

        # 简单情况：按名称匹配
        if hasattr(student_layer, "name") and hasattr(self.teacher_model, "get_layer"):
            try:
                return self.teacher_model.get_layer(student_layer.name)
            except:
                pass

        # 更复杂的情况：按类型和位置匹配
        # 这需要适配器实现
        return None

    def evaluate(self, original: Any, compressed: Any) -> Dict:
        """
        评估蒸馏前后的层性能

        Args:
            original: 原始层
            compressed: 蒸馏后的层

        Returns:
            性能评估指标
        """
        metrics = {}

        try:
            # 对于蒸馏，主要评估指标是与教师模型的输出相似性
            # 这通常需要一些验证数据

            # 在没有数据的情况下，可以评估权重差异
            if hasattr(original, "weight") and hasattr(compressed, "weight"):
                # 计算权重变化
                weight_diff = np.mean(np.abs(original.weight - compressed.weight))
                metrics["weight_diff"] = float(weight_diff)

            # 添加蒸馏信息
            if hasattr(compressed, "distillation_info"):
                metrics["distillation_info"] = compressed.distillation_info

        except Exception as e:
            self.logger.error(f"Evaluation failed: {str(e)}")

        return metrics

    def can_apply(self, layer_type: str) -> bool:
        """检查知识蒸馏是否适用于该层类型"""
        # 知识蒸馏适用于大多数类型的层，尤其是具有学习参数的层
        applicable_types = [
            "Conv1d", "Conv2d", "Conv3d",
            "Linear", "Dense",
            "LSTM", "GRU", "RNN",
            "Transformer", "MultiHeadAttention",
            "Embedding"
        ]
        return layer_type in applicable_types

    @classmethod
    def get_default_params(cls) -> Dict:
        """获取默认蒸馏参数"""
        return {
            "temperature": 2.0,
            "alpha": 0.5,
            "features": False,
            "iterations": 100,
            "batch_size": 64
        }

    def get_expected_metrics(self, layer_info: Dict) -> Dict:
        """
        获取知识蒸馏的预期性能影响

        Args:
            layer_info: 层的信息

        Returns:
            预期的性能指标变化
        """
        # 知识蒸馏通常不直接减少模型大小，但可以帮助保持精度
        # 其主要目标是将复杂模型的知识转移到简单模型中

        # 蒸馏本身不改变模型大小
        size_ratio = 1.0

        # 蒸馏后的模型通常保持较高精度
        accuracy_ratio = 0.95

        # 蒸馏本身不影响推理延迟
        latency_ratio = 1.0

        # 构建预期指标
        return {
            "accuracy_ratio": accuracy_ratio,
            "size_ratio": size_ratio,
            "latency_ratio": latency_ratio,
            "memory_ratio": size_ratio
        }